U.S. patent number 8,717,434 [Application Number 13/398,268] was granted by the patent office on 2014-05-06 for method and system for collecting and analyzing operational information from a network of components associated with a liquid energy commodity.
This patent grant is currently assigned to Genscape Intangible Holding, Inc.. The grantee listed for this patent is Deirdre Alphenaar, Walter F. Jones, Abudi Zein. Invention is credited to Deirdre Alphenaar, Walter F. Jones, Abudi Zein.
United States Patent |
8,717,434 |
Alphenaar , et al. |
May 6, 2014 |
Method and system for collecting and analyzing operational
information from a network of components associated with a liquid
energy commodity
Abstract
A method for collecting and analyzing operational information
from a network of components associated with a liquid energy
commodity comprises the steps of: (a) measuring an amount of the
liquid energy commodity in storage at one or more storage
facilities in the network, and storing that measurement data; (b)
determining a flow rate of the liquid energy commodity in one or
more selected pipelines in the network, and storing that flow rate
data; (c) ascertaining an operational status of one or more
processing facilities in the network, and storing that operational
status information; (d) analyzing the measurement data, the flow
rate data, and the operational status information to determine a
balance of the liquid energy commodity in the network or a selected
portion thereof at a given time; and (e) communicating information
about the balance of the liquid energy commodity to a third-party
market participant.
Inventors: |
Alphenaar; Deirdre (Prospect,
KY), Jones; Walter F. (Crestwood, KY), Zein; Abudi
(Jersey City, NJ) |
Applicant: |
Name |
City |
State |
Country |
Type |
Alphenaar; Deirdre
Jones; Walter F.
Zein; Abudi |
Prospect
Crestwood
Jersey City |
KY
KY
NJ |
US
US
US |
|
|
Assignee: |
Genscape Intangible Holding,
Inc. (Louisville, KY)
|
Family
ID: |
46636624 |
Appl.
No.: |
13/398,268 |
Filed: |
February 16, 2012 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20120206595 A1 |
Aug 16, 2012 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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61443510 |
Feb 16, 2011 |
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Current U.S.
Class: |
348/135 |
Current CPC
Class: |
F17D
3/00 (20130101); F17D 3/01 (20130101); G06Q
10/0631 (20130101); F17D 3/18 (20130101); G01F
23/2925 (20130101); F17D 1/08 (20130101); G01F
15/06 (20130101); G01F 1/00 (20130101); G01F
23/00 (20130101); Y02P 90/82 (20151101) |
Current International
Class: |
H04N
7/18 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
Other References
ISR/KR, International Search Report and Written Opinion issued in
corresponding International Patent Application No. PCT/US12/25418,
mailed Sep. 19, 2012. cited by applicant .
ISA/KR, International Preliminary Report on Patentability issued in
related international application No. PCT/US2012/025418, mailed
Aug. 29, 2013. cited by applicant.
|
Primary Examiner: Perungavoor; Sath V
Assistant Examiner: Luo; Kate
Attorney, Agent or Firm: Stites & Harbison, PLLC Nagle,
Jr.; David W.
Claims
What is claimed is:
1. A method for collecting and analyzing operational information
from a network of components associated with a liquid energy
commodity, comprising the steps of: measuring an amount of the
liquid energy commodity in storage at one or more storage
facilities in the network, and storing that measurement data in a
first database at a central data processing facility; determining a
flow rate of the liquid energy commodity in one or more selected
pipelines in the network, and storing that flow rate data in a
second database at the central data processing facility, wherein,
as part of the step of determining the flow rate of the liquid
energy commodity in a selected pipeline in the network, a power
monitoring device is positioned to monitor power lines supplying
electric power to a particular pumping station on the selected
pipeline, said power monitoring device including sensing elements
responsive to electric potential and magnetic flux densities
associated with the power lines, therefore allowing for a
measurement of electric potential and magnetic flux densities
associated with the power lines, and thus a determination of power
consumed by the particular pumping station; ascertaining an
operational status of one or more processing facilities in the
network, and storing that operational status information in a third
database at the central data processing facility; analyzing the
measurement data, the flow rate data, and the operational status
information to determine a balance of the liquid energy commodity
in the network or a selected portion thereof at a given time; and
communicating information about the balance of the liquid energy
commodity to a third-party market participant.
2. The method as recited in claim 1, in which the liquid energy
commodity is crude oil.
3. The method as recited in claim 1, in which the first database,
the second database, and the third database are integrated into a
single database at the central data processing facility.
4. The method as recited in claim 1, in which the step of measuring
the amount of the liquid energy commodity in storage comprises the
sub-steps of: periodically conducting an inspection of one or more
tanks of a particular storage facility, including collecting one or
more images of each tank; transmitting the collected images of each
tank to the central data processing facility; and analyzing the
collected images of each tank to determine a liquid level for each
tank.
5. The method as recited in claim 4, wherein the collected images
are infrared images acquired by a thermal imaging camera.
6. The method as recited in claim 5, wherein a method of detecting
edges is applied to each collected image to find the location of
tanks in each collected image and then to identify the liquid level
in each tank.
7. The method as recited in claim 1, wherein, as part of the step
of ascertaining the operational status of one or more processing
facilities in the network, a thermal imaging camera is positioned
to acquire thermal data from one or more units of a selected
processing facility.
8. The method as recited in claim 7, wherein the thermal imaging
camera is positioned to acquire thermal data from one or more
stacks of the selected processing facility.
9. A method for collecting and analyzing operational information
from a network of components associated with a liquid energy
commodity, comprising the steps of: using a thermal imaging camera
to collect images at one or more storage facilities in the network,
transmitting the collected images to a central data processing
facility, and analyzing the collected images to measure an amount
of the liquid energy commodity in storage at each of the one or
more storage facilities, and storing that measurement data in a
first database at the central data processing facility; positioning
one or more power monitoring devices to monitor power lines
supplying electric power to particular pumping stations associated
with selected pipelines in the network, each of the one or more
power monitoring devices including sensing elements responsive to
electric potential and magnetic flux densities associated with the
power lines, therefore allowing for a measurement of electric
potential and magnetic flux densities associated with the power
lines, and thus a determination of power consumed by each
particular pumping station, which, is then correlated to a flow
rate of the liquid energy commodity in each selected pipeline in
the network, and storing that flow rate data in a second database
at the central data processing facility; using a thermal imaging
camera to ascertain an operational status of one or more processing
facilities in the network, and storing that operational status
information in a third database at the central data processing
facility; analyzing the measurement data, the flow rate data, and
the operational status information to determine a balance of the
liquid energy commodity in the network or a selected portion
thereof at a given time; and communicating information about the
balance of the liquid energy commodity to a third-party market
participant.
10. The method as recited in claim 9, in which the liquid energy
commodity is crude oil.
11. The method as recited in claim 9, in which the first database,
the second database, and the third database are integrated into a
single database at the central data processing facility.
12. A system for collecting and analyzing operational information
from a network of components associated with a liquid energy
commodity, comprising: a storage measurement module for receiving
and analyzing collected images of one or more storage facilities to
measure an amount of the liquid energy commodity in storage at each
of the one or more storage facilities, storing such measurement
data in a first database; a flow rate determination module for
receiving and processing measurements of electric potential and
magnetic flux densities associated with power lines for pumping
stations on one or more pipelines to determine a flow rate of the
liquid energy commodity in each of said one or more pipelines,
storing such flow rate data in a second database; an operational
status module for receiving and processing information about an
operational status of a processing facility, storing that
operational status information in a third database; an analysis
module for querying the first database, the second database, and
the third database and analyzing the measurement data, the flow
rate data, and the operational status information to determine a
balance of the liquid energy commodity in the network or a selected
portion thereof at a given time; and a communications module for
communicating information about the liquid energy commodity to a
third-party market participant.
13. The system as recited in claim 12, in which the first database,
the second database, and the third database are integrated into a
single database.
14. A method for collecting and analyzing operational information
from a network of components associated with crude oil transport,
comprising the steps of: using a thermal imaging camera to collect
images of one or more storage tanks in the network, transmitting
the collected images to a central data processing facility, and
analyzing the collected images to measure an amount of crude oil in
the one or more storage tanks, and storing that measurement data in
a first database at the central data processing facility;
positioning one or more power monitoring devices to monitor power
lines supplying electric power to particular pumping stations
associated with selected pipelines in the network, each of the one
or more power monitoring devices including sensing elements
responsive to electric potential and magnetic flux densities
associated with the power lines, therefore allowing for a
measurement of electric potential and magnetic flux densities
associated with the power lines, and thus a determination of power
consumed by each particular pumping station, which, is then
correlated to a flow rate of crude oil in each selected pipeline in
the network, and storing that flow rate data in a second database
at the central data processing facility; using a thermal imaging
camera to ascertain an operational status of one or more processing
facilities in the network, and storing that operational status
information in a third database at the central data processing
facility; analyzing the measurement data, the flow rate data, and
the operational status information to determine a balance of crude
oil in the network or a selected portion thereof at a given time;
and communicating information about the balance of crude oil to a
third-party market participant.
15. The method as recited in claim 14, in which the first database,
the second database, and the third database are integrated into a
single database at the central data processing facility.
16. The method as recited in claim 14, in which information
communicated to the third-party market participant is an amount of
crude oil in storage in the network at the given time.
17. The method as recited in claim 14, in which information
communicated to the third-party market participant is an amount of
crude oil flowing into the network over a given time period.
18. The method as recited in claim 14, in which information
communicated to the third-party market participant is an amount of
crude oil flowing out of the network over a given time period.
19. A method for monitoring transport of crude oil in a network
that includes a production source, a pipeline, a processing
facility, and one or more storage tanks, comprising the steps of:
positioning one or more power monitoring devices to monitor power
lines supplying electric power to selected pumping stations
associated with the pipeline extending between the production
source and the processing facility, each of the one or more power
monitoring devices including sensing elements responsive to
electric potential and magnetic flux densities associated with the
power lines, therefore allowing for a measurement of electric
potential and magnetic flux densities associated with the power
lines, and thus a determination of power consumed by each selected
pumping station, which, is then correlated to a flow rate of crude
oil in the pipeline, and storing that flow rate data in a first
database at a central data processing facility; using a thermal
imaging camera to ascertain an operational status of the processing
facility; and storing that operational status information in a
second database at the central data processing facility; using a
thermal imaging camera to collect images of the one or more storage
tanks, transmitting the collected images to the central data
processing facility, and analyzing the collected images to measure
an amount of crude oil in the one or more storage tanks, and
storing that measurement data in a third database at the central
data processing facility; analyzing the flow rate data, the
operational status information, and the measurement data to
determine a balance of crude oil in the network at a given time;
and communicating information about the balance of crude oil to a
third-party market participant.
20. A method for collecting and analyzing operational information
from a network of components associated with a liquid energy
commodity, comprising the steps of: measuring an amount of the
liquid energy commodity in storage at one or more storage
facilities in the network, and storing that measurement data in a
first database at a central data processing facility; estimating a
flow rate of the liquid energy commodity in one or more selected
pipelines in the network based on power consumed by one or more
pumping stations associated with the one or more selected
pipelines, and storing that flow rate data in a second database at
the central data processing facility, wherein, as part of the step
of estimating the flow rate of the liquid energy commodity in a
selected pipeline in the network, a power monitoring device is
positioned to monitor power lines supplying electric power to a
particular pumping station on the selected pipeline, said power
monitoring device including sensing elements responsive to electric
potential and magnetic flux densities associated with the power
lines, therefore allowing for a measurement of electric potential
and magnetic flux densities associated with the power lines, and
thus a determination of power consumed by the particular pumping
station; ascertaining an operational status of one or more
processing facilities in the network, and storing that operational
status information in a third database at the central data
processing facility; analyzing the measurement data, the flow rate
data, and the operational status information to determine a balance
of the liquid energy commodity in the network or a selected portion
thereof at a given time; and communicating information about the
balance of the liquid energy commodity to a third-party market
participant.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
The present application claims priority to U.S. Provisional Patent
Application Ser. No. 61/443,510 filed on Feb. 16, 2011, the entire
disclosure of which is incorporated herein by reference.
BACKGROUND OF THE INVENTION
The present invention is a method and system for collecting and
analyzing operational information from a network of components
associated with a liquid energy commodity, such as crude oil or
natural gas liquid (NGL).
Liquid energy commodities, such as crude oil, comprise a
multi-billion dollar economic market. These commodities are bought
and sold by many parties, and as with any traded market,
information about the traded commodities is very valuable to market
participants. Specifically, the operations of the various
components and facilities of the production, transportation,
storage, and distribution systems for each of these commodities can
have significant impacts on the price and availability of these
commodities, making information about said operations valuable.
Furthermore, such information generally is not disclosed publicly
by the various component owners or operators, and access to said
information is therefore limited.
Certain data is collected by organizations such as the United
States Energy Information Administration ("EIA"), typically via
surveys of selected owners and/or operators. However, the length of
time required to collect and compile this data and then disseminate
it to the public or market participants can range from days to
months, so that the collected and compiled data is usually delayed
and of limited value for short-term trading purposes.
SUMMARY OF THE INVENTION
The present invention is a method and system for collecting and
analyzing operational information from a network of components
associated with a liquid energy commodity, such as crude oil or
natural gas liquid (NGL).
In accordance with the method and system of the present invention,
sensors or measurement devices are deployed at various points in a
network to collect data. The method then generally comprises the
steps of: (a) measuring an amount of the liquid energy commodity in
storage at one or more storage facilities in the network, and
storing that measurement data in a first database at a central data
processing facility; (b) determining a flow rate of the liquid
energy commodity in one or more selected pipelines in the network,
and storing that flow rate data in a second database at the central
data processing facility; (c) ascertaining an operational status of
one or more processing facilities in the network, and storing that
operational status information in a third database at the central
data processing facility; (d) analyzing the measurement data, the
flow rate data, and the operational status information to determine
a balance of the liquid energy commodity in the network or a
selected portion thereof at a given time; and (e) communicating
information about the balance of the liquid energy commodity to a
third-party market participant.
With respect to storage facilities, at each selected storage
facility in a particular network, there is a measurement of the
amount of crude oil or other liquid energy commodity in storage.
For instance, most crude oil is stored in large, above-ground tanks
that either have: a floating roof, which is known as an External
Floating Roof (EFR); or a fixed roof with a floating roof internal
to the tank, which is known as an Internal Floating Roof (IFR).
Thus, each tank in a particular location can be researched using
publicly available resources or visual inspection, and all relevant
information about each tank, including volume capacity information,
tank type (i.e., floating roof or fixed roof), and physical
dimensions, is stored in a database. Then, on a predetermined
schedule, an inspection of each tank at the particular location is
conducted that includes the collection of one or more photographic
images (i.e., visible spectrum) or video of each tank, and/or the
collection of infrared images or video of each tank. The collected
photographic images and the collected infrared images of each tank
are then transmitted to a central processing facility for analysis
to obtain a measurement of the amount of crude oil or other liquid
energy commodity in storage.
With respect to pipelines, in order to maintain the pressure of the
liquid energy commodity, pumping stations are positioned along
pipelines. The pumps used at each of these pumping stations are
typically electrically driven induction motors. In order to perform
a remote determination of the amount and rate of flow in a
particular pipeline at a given time, one preferred form of analysis
is based on monitoring the real-time electric power consumption of
some number of pumping stations along a selected pipeline. In one
exemplary implementation, a monitoring device is deployed and used
to monitor one or more power lines supplying electric power to each
selected pumping station. The monitoring device is primarily
comprised of sensing elements responsive to electric potential and
the magnetic flux densities associated with the one or more power
lines, therefore allowing for periodic or continuous measurements
of the electric potential and magnetic flux densities associated
with the one or more power lines, and thus a determination of
power. Data from such monitoring devices is then transmitted to the
central data processing facility. At the central data processing
facility, a model of the pipelines and the pumping stations in the
particular network is developed that includes computations of the
elevation gain or loss between any monitored pumping station and
the next pumping station downstream using standard geographical
elevation data. The pressure differential between the output or
discharge side of any particular monitored pumping station and the
input side of the next station downstream is then estimated. A
range of possible flow rates for the pipeline from minimum possible
flow to maximum possible flow for the pipeline is plotted versus
the equivalent expected power consumption at the monitored pumping
station.
Once such power consumption determinations have been made for any
particular pumping station, power changes at each pumping station
can be correlated to changes in flow through each pumping station.
Thus, since the monitoring devices described above allow for
periodic or continuous measurements of power consumed at a
particular pumping station, the collected data from those
monitoring devices can be used to determine flow through and
between pumping stations.
With respect to processing facilities, a liquid energy commodity
enters a refinery or other processing facility at some point in the
network. In the method and system of the present invention, the
operational status of such processing facilities is ascertained.
One preferred method for monitoring the operation of processing
facilities is by using fixed thermal imaging cameras. A thermal
imaging camera can acquire thermal data and record images of
emissions and heat signatures of various key units that can be used
to ascertain whether a processing facility is functioning as
expected or not.
With data and information about the three fundamental components of
a particular network--(i) storage facilities, (ii) pipelines, and
(iii) processing facilities--it is possible to determine total
"balances" of the liquid energy commodity. For example, "balances"
of interest to market participants with respect to crude oil
include, but are not limited to: the amount of crude oil in storage
in a given market region at a given time; the amount of crude oil
flowing into a market region from adjacent market regions; and/or
the amount of crude oil being processed into gasoline and other
petroleum products. Once such an analysis has been completed,
information about the balance of the crude oil or other liquid
energy commodity in the network can be communicated to market
participants and other interested parties, i.e., third parties who
would not ordinarily have ready access to such information.
DESCRIPTION OF THE DRAWINGS
FIG. 1 is a schematic view of an exemplary network associated with
crude oil production;
FIG. 2 is a schematic view of an exemplary network associated with
crude oil transport and processing;
FIG. 3 is an exemplary image in which the outline of three tanks
has been found and marked on the image using the Sobel edge
detection method;
FIG. 4 is an exemplary image with the outline of three tanks from
FIG. 3 overlaid on a subsequent collected image;
FIG. 5 is a graph that illustrates the one-dimensional shape of an
edge in an image;
FIG. 6 includes a pair of 3.times.3 convolution masks used in a
Sobel edge detection method;
FIG. 7 illustrates how a convolution mask in a Sobel edge detection
method is applied to an input image;
FIG. 8 is a plot of flow rate (barrels per day) against expected
power consumption (MW) for an exemplary pumping station;
FIGS. 9(a)-(d) are a series of thermal images illustrating the
ramp-down of a fluid catalytic cracking unit at a refinery;
FIG. 10 illustrates a storage hub that is connected to three
pipelines;
FIG. 11 is a plot illustrating how directly measured data is fitted
using a standard mathematical regression model to historic
data;
FIG. 12 is a schematic view of another exemplary network associated
with crude oil production;
FIG. 13 is a flow chart depicting the general functionality of an
implementation of the method and system of the present invention in
connection with the exemplary network of FIG. 12; and
FIG. 14 is a schematic representation of the core components in an
exemplary implementation of the method and system of the present
invention.
DETAILED DESCRIPTION OF THE INVENTION
The present invention is a method and system for collecting and
analyzing operational information from a network of components
associated with a liquid energy commodity, such as crude oil or
natural gas liquid (NGL).
For example, since crude oil is a fossil fuel, it is typically
drilled or mined at locations where there are naturally occurring
deposits or reservoirs. Once collected at such a location (for
example, from a well), the crude oil is typically pumped directly
into a pipeline or stored in above-ground storage (e.g., tanks) or
underground storage (e.g., salt dome caverns). From such storage
facilities, it can then be transported via pipelines to refineries
or other processing facilities for processing. Thus, there is an
interconnected network of crude oil wells, crude oil pipelines,
crude oil storage facilities, and crude oil refineries.
For another example, natural gas is extracted at locations where
there are naturally occurring reservoirs. The extracted natural gas
is then processed into "dry" natural gas or "wet" natural gas at
processing plants, the latter of which is termed natural gas liquid
(NGL). The NGL is then transported using NGL pipelines and stored
at NGL storage sites. The NGL may then be separated into at what
are termed "purity" products such as ethane, propane, and butane.
These NGL products can then be processed at ethylene cracking
facilities which take NGL products in and process them into
petrochemical industry feedstocks such as ethylene, propylene,
etc.
In accordance with the method and system of the present invention,
sensors or measurement devices are deployed at various points in a
network to collect data. The method then generally comprises the
steps of: (a) measuring an amount of the liquid energy commodity in
storage at one or more storage facilities in the network, and
storing that measurement data in a first database at a central data
processing facility; (b) determining a flow rate of the liquid
energy commodity in one or more selected pipelines in the network,
and storing that flow rate data in a second database at the central
data processing facility; (c) ascertaining an operational status of
one or more processing facilities in the network, and storing that
operational status information in a third database at the central
data processing facility; (d) analyzing the measurement data, the
flow rate data, and the operational status information to determine
a balance of the liquid energy commodity in the network or a
selected portion thereof at a given time; and (e) communicating
information about the balance of the liquid energy commodity to a
third-party market participant.
As will become clear in the description that follows, many of the
operational steps of the method and system of the present
invention, including the collection of data and the various
computational steps associated with the analysis of that collected
data, are preferably achieved through the use of a digital computer
program, i.e., computer-readable instructions stored and executed
by a computer. Thus, execution of the requisite routines and
subroutines can be carried out using standard programming
techniques and languages. With benefit of the following
description, such programming is readily accomplished by one of
ordinary skill in the art.
For example, with respect to crude oil, there is an interconnected
network of crude oil wells, crude oil pipelines, crude oil storage
facilities, and crude oil refineries. For purposes of the
subsequent discussion, and as shown in FIGS. 1 and 2, a "network"
for crude oil can thus be characterized as having three fundamental
components: (i) crude oil storage facilities; (ii) crude oil
pipelines; and (iii) crude oil refineries or other processing
facilities. Understanding and collecting information about the
operation of these components and the flow of crude oil between
these components allows for the modeling of the network and
monitoring of the real-time network dynamics. In other words, by
taking certain physical measurements of the crude oil (or other
liquid energy commodity) at various points in the network, it is
possible to determine total "balances" of the crude oil in
different functional parts of the network. For example, "balances"
of interest to market participants with respect to crude oil
include, but are not limited to: the amount of crude oil in storage
in a given market region at a given time; the amount of crude oil
flowing into a market region from adjacent market regions; and/or
the amount of crude oil being processed into gasoline and/or other
petroleum products.
Referring still to the crude oil market, in the U.S., the amount of
crude oil stored in tanks located at either terminals, storage
hubs, or oil refineries (including crude oil in transit in
pipelines) is of the order of 340 million barrels. 55,000 miles of
pipelines transport crude oil from American production wells
(notably in the states of Texas, Louisiana, Oklahoma, and Wyoming),
import terminals (notably seaports in the Gulf of Mexico), or
overland across the border from Canada to various regional markets.
These markets are divided into five large regions in the U.S. known
as Petroleum Administration for Defense (PAD) Districts. Crude oil
pipelines typically vary in diameter from eight to thirty inches.
Larger inter-regional pipelines, which service refineries or
storage hubs, are generally more relevant to overall market
dynamics than smaller intra-regional pipelines. The market
relevance of crude oil in storage varies depending on the purpose
of the crude oil being stored. For example, crude oil stored at
refineries is available for refining at any particular point in
time into gasoline and/or other petroleum products. Crude oil
stored at major oil storage hubs may be indicative of the amount of
crude oil being stored by financial speculators or by suppliers to
refineries downstream of the storage hub. Other tank storage may be
primarily used to maintain appropriate pressures and volumes in
order to successfully operate the required flow dynamics on a
particular pipeline.
Storage Facilities
In accordance with the method and system of the present invention,
at each selected storage facility in a particular network, there is
a measurement of the amount of crude oil or other liquid energy
commodity in storage. For instance, one preferred method for
measuring the amount of crude oil being stored in a particular tank
is described in commonly owned and co-pending U.S. patent
application Ser. No. 13/089,674 entitled "Method and System for
Determining an Amount of Crude Oil Stored in a Particular
Location," which is incorporated herein by reference.
As described in U.S. patent application Ser. No. 13/089,674, most
crude oil is stored in large, above-ground tanks that either have:
a floating roof, which is known as an External Floating Roof (EFR);
or a fixed roof with a floating roof internal to the tank, which is
known as an Internal Floating Roof (IFR). Thus, each tank in a
particular location can be researched using publicly available
resources or visual inspection, and all relevant information about
each tank, including volume capacity information, tank type (i.e.,
floating roof or fixed roof), and physical dimensions, is stored in
a database. Then, on a predetermined schedule, an inspection of
each tank at the particular location is conducted that includes the
collection of one or more photographic images (i.e., visible
spectrum) or video of each tank, and/or the collection of infrared
images or video of each tank. Such images can be collected by
aerial means, through use of ground-placed, fixed cameras, or via
satellite imaging. In the case of an aerial image acquisition, such
as a helicopter flyover, the helicopter preferably flies a defined
and repeatable flight route and adheres to a pre-defined sequence
for image acquisition, which facilitates the subsequent analysis.
Alternatively, fixed thermal imaging cameras may take infrared
images at predetermined intervals. In any event, the collected
photographic images and the collected infrared images of each tank
are then transmitted to a central processing facility for
analysis.
With respect to the analysis of a tank with a floating roof, one
preferred form of analysis is to determine the height of the roof
relative to the top of the selected tank using standard image pixel
number determination techniques. For example, tank levels can be
measured by drawing two vertical lines, such as L1 and L2. When
measuring tank levels for floating roof tanks, the L1 line is drawn
on the inside of the tank from the top of the tank down to the top
of the lid and approximates the height the roof has been lowered.
The L2 line is drawn on the outside of the tank from the top of the
tank down to the bottom of the tank and approximates the height of
the tank. The respective lengths of the L1 and L2 lines are then
measured. Such measurement is optimized, for example, by ensuring
an appropriate camera angle and distance from the tank, using
high-resolution equipment for image acquisition, and/or ensuring
consistent and proper placement of the L1 and L2 lines on the
image.
Based on the determined height of the roof (which is indicative of
the liquid level) and the stored volume capacity information and/or
the stored physical dimensions of the selected tank, the amount of
crude oil in the tank can be calculated. For example, if the roof
is at the halfway point, i.e., at a 50% height relative to the top
of a 200,000-barrel tank, and the tank has a typical cylindrical
construction with a constant diameter from the base to the top, it
is calculated that 100,000 barrels of crude oil are in the tank.
Stated another way, tank level percent of capacity can be
calculated by 1-(D1/D2), where D1 and D2 are the respective
measured lengths of L1 and L2 in image pixels. Tank level percent
of capacity is then multiplied by the tank capacity to calculate
the number of barrels of crude oil in the tank.
With respect to the analysis of a tank with a floating roof, in
another preferred form of image analysis, the top, roof, and the
base of a tank are identified in either a photographic image or
infrared image. Automated elliptical form fitting or detection
algorithms employing mathematical transformations, such as a Hough
transform, can then be used to fit an elliptical plane on each of
the top, roof, and the base of the tank. Based on the determined
height of the roof relative to the base and/or the top of the tank
(which again is indicative of the liquid level) and the stored
volume capacity information and/or the stored physical dimensions
of the selected tank, the amount of crude oil in the tank can again
be calculated.
With respect to tanks with fixed roofs, the liquid level within a
selected tank can be ascertained from the collected infrared
images, as the temperature of the stored oil is different than that
of the gas above it in the tank. One preferred form of analysis to
determine the height of the liquid level in the tank is to measure
the pixel distance from the liquid-gas boundary to the base of the
tank. Based on the ascertained liquid level within the tank and the
stored volume capacity information and/or the stored physical
dimensions of the selected tank, the amount of crude oil in the
tank can again be calculated.
Furthermore, with respect to tanks with fixed roofs and
determination of liquid level from the collected infrared images,
one particular method of analysis is described in detail below.
In this particular method of analysis, infrared images are
collected for each tank of interest at selected intervals (e.g.,
every five minutes) and transmitted to the central data processing
facility for analysis. Although the camera that collects the
infrared images is preferably fixed in position, it is acknowledged
that there is often some minor movement of the camera. Thus,
feature detection is used to find the tank location in each
infrared image, thus ensuring an accurate calculation of the amount
of crude oil in the tank.
Edges in images are areas with strong intensity contrasts, i.e., a
significant change in intensity from one pixel to the next. There
are various methods and techniques for detecting edges in an image,
which can be generally grouped into two categories: gradient and
Laplacian methods. A gradient method detects the edges by looking
for the maximum and minimum in the first derivative of the image. A
Laplacian method searches for zero crossings in the second
derivative of the image to find edges.
Referring now to FIG. 5, an edge has the one-dimensional shape of a
ramp. Employing a gradient method, the derivative of the
one-dimensional shape thus shows a maximum located at the center of
the edge. Based on this one-dimensional analysis, the theory can be
carried over to two dimensions as long as there is an accurate
approximation to calculate the derivative of a two-dimensional
image. In this case, a Sobel operator is used to perform a
two-dimensional spatial gradient measurement on a particular
infrared image in order to find the approximate absolute gradient
magnitude at each point in the infrared image. See R. Gonzalez and
R. Woods, Digital Image Processing, Addison Wesley (1992), pp.
414-428. The Sobel edge detection method uses a pair of 3.times.3
convolution masks (FIG. 6), one estimating the gradient in the
x-direction (columns) (Gx) and the other estimating the gradient in
the y-direction (rows) (Gy). A convolution mask is usually much
smaller than the actual image. As a result, the mask is applied and
slid over the image, manipulating a square of pixels at a time.
Specifically, in use, the mask is slid over an area of the input
image (from the beginning of a row), changes the value of the pixel
and shifts one pixel to the right, and then continues to the right
until it reaches the end of the row. It then starts at the
beginning of the next row. FIG. 7 illustrates how a convolution
mask in a Sobel edge detection method is applied to an input image,
with the mask being applied over the top left portion of the input
image and equation (1) below being used to calculate a particular
pixel in the output image. The center of the mask is placed over
the pixel that is being manipulated in the image; for example,
pixel (a.sub.22) is converted to pixel (b.sub.22) by:
b.sub.22=(a.sub.11*m.sub.11)+(a.sub.12*m.sub.12)+(a.sub.13*m.sub.13)+(a.s-
ub.21*m.sub.21)+(a.sub.22*m.sub.22)+(a.sub.23*m.sub.123)+(a.sub.31*m.sub.3-
1)+(a.sub.32*m.sub.32)+(a.sub.33*m.sub.33) (1)
The Gx mask highlights the edges in the horizontal direction, while
the Gy mask highlights the edges in the vertical direction. After
taking the magnitude of both and adding, the resulting output
detects edges in both directions.
In practice, a Sobel edge detection image is computed for each
collected infrared image. Then, for each edge detection image, the
tank location is found by determining the best fit to one or more
features. Each feature is a set of pixel locations where the Sobel
edge detection image should contain an edge and have a black color.
FIG. 3 is an exemplary image in which the outline of three tanks
has been found and marked on the image using the Sobel edge
detection method, and FIG. 4 shows how this outline of three tanks
can be overlaid on a subsequent collected image.
In this particular method of analysis, after finding the tank
location, the tank level is computed based on a vertical line
starting at the bottom of each tank, as also shown in FIG. 4. Each
vertical line is searched upward from the bottom of the tank for
the next edge location, which is the oil level location. The number
of pixels between the bottom of the tank and the oil level location
(pixel height) is used to compute the tank percent full as follows:
Percent Full=100*(pixel height)/(total pixel height of tank)
(2)
Furthermore, in this particular method of analysis, the flow rate
with respect to a certain tank can be calculated by the rate of
change of the storage levels within the tank:
Flow.sub.--i=(S.sub.--i-S.sub.--i-1)*Tank_Capacity*24/100 (3) where
S.sub.--i=(L.sub.--i+L.sub.--i-1+L.sub.--i-2+L.sub.--i-3+L.sub.--i-4+L.su-
b.--i-5+L.sub.--i-6)/7 (4) and
S.sub.--i-1=(L.sub.--i-1+L.sub.--i-2+L.sub.--i-3+L.sub.--i-4+L.sub.--i-5+-
L.sub.--i-6+L.sub.--i-7)/7 (5) where Tank_Capacity is in barrels,
and L_i is the tank Percent Full at hour i. When Flow_i<0, then
Flow_i is set to zero since only oil flowing in to a tank is being
considered.
No matter which technique of analysis is employed, the objective
again is to obtain a measurement of the amount of crude oil in
storage in the particular network, which is stored at the central
data processing facility.
With respect to storage of NGL products, such as ethane, propane,
and butane, similar image collection and analysis on the vertical
and horizontal tanks commonly used to store such products can be
carried out in order to obtain a measurement of the amount of the
NGL product in storage in the particular network.
Pipelines
Along with measuring the amount of crude oil or other liquid energy
commodity in storage in a particular network, there is a
determination of the amount and rate of flow in selected pipelines
in the particular network.
For instance, a large inter-regional crude oil pipeline typically
runs for hundreds of miles. In order to maintain the pressure of
the flowing crude oil, crude oil pumping stations are typically
constructed every 80-100 miles. The pumps used at each of these
pumping stations are typically electrically-driven induction
motors, with horsepower (hp) ranging from 500-4500 hp. Crude oil
pipeline flow data in real-time is generally only known to the
owners, operators, and shippers on the pipeline. In order to
perform a remote determination of the amount and rate of oil flow
in a particular pipeline at a given time, one preferred form of
analysis is based on monitoring the real-time electric power
consumption of some number of pumping stations along a selected
pipeline.
Specifically, in one exemplary implementation, a monitoring device
is deployed and used to monitor one or more power lines supplying
electric power to each selected pumping station. The monitoring
device (also referred to herein as "power monitoring device") is
primarily comprised of sensing elements responsive to electric
potential and the magnetic flux densities associated with the one
or more power lines, therefore allowing for periodic or continuous
measurements of the electric potential and magnetic flux densities
associated with the one or more power lines, and thus a
determination of power. The construction and use of such monitoring
devices is described in commonly owned U.S. Pat. No. 6,771,058
entitled "Apparatus and Method for the Measurement and Monitoring
of Electrical Power Generation and Transmission," and U.S. Pat. No.
6,714,000 entitled "Apparatus and Method for Monitoring Power and
Current Flow," each of which is incorporated herein by
reference.
Data from such monitoring devices is then transmitted to a central
data processing facility. At the central data processing facility,
a model of the pipelines and the pumping stations in the particular
network is developed that includes computations of the elevation
gain or loss between any monitored pumping station and the next
pumping station downstream using standard geographical elevation
data. The pressure differential between the output or discharge
side of any particular monitored pumping station and the input side
of the next station downstream is then estimated. The pressure
change calculations also take typical minimum and maximum pressures
for the pipeline into account for use as reasonable computation
boundary values.
For example, a preferred flow model takes into account the pipeline
length and elevation change between a monitored pumping station and
the next pumping station downstream. The pipeline length, elevation
change, and power usage are used to estimate the pressure
differential between the output side of the first pumping station
and the input side of the next pumping station downstream. In other
words, the frictional pressure differential or head loss (HeadLoss
(H) in feet) between any two pumping stations on a selected
pipeline can be calculated from the variables set forth below. See
Pipeline Rules of Thumb Handbook, Gulf Professional Publishing (5th
Edition) (2001).
Sg=Specific Oil Gravity (API)
Q=Flow Rate (gal/min)
H=Head Differential at Pump (ft)
D=Diameter of Pipe (ft)
L=Length of Pipeline Segment (ft)
E=Pump Efficiency
V=Oil Velocity (ft/sec)
KV=Kinematic Viscosity (cSt)
HeadLoss=Head Loss (ft)
The flow rate (Q) values range from zero to the maximum flow rate
of the pipeline. The flow rate (Q) is related to oil velocity as
follows:
.times..times..function..pi. ##EQU00001## To obtain the kinematic
viscosity, a CentiStokes (cSt) value is based on an assumption of
API and temperature, and then is converted into units of
(ft.sup.2/sec):
.times..times..function. ##EQU00002## Fanning's equation is then
used to compute the frictional pressure drop (HeadLoss) between
pumping stations for a given flow rate (Q), pipeline segment length
(L) and elevation profile. Fanning's equation for expressing the
frictional pressure drop of oil flowing in a pipeline is a function
of a frictional loss (f) derived from the Reynolds number (Re).
.function..times..times..times..times..times..times..times..times..times.-
.times..ltoreq..times..times..times..times..times..times..times..times.
##EQU00003##
TABLE-US-00001 TABLE 1 Re > 2200 (Turbulent Flow) Re f 2500
0.045 3000 0.043 3500 0.041 4000 0.039 4500 0.038 5000 0.036 5500
0.035 6000 0.035 7000 0.033 8000 0.032 9000 0.031 10000 0.03 15000
0.027 20000 0.025 25000 0.024 30000 0.023 40000 0.021 50000 0.02
60000 0.019 80000 0.018 100000 0.017 150000 0.016 200000 0.015
1000000 0.012 10000000 0.01
The hydraulic horsepower required to pump oil along a particular
pipeline segment is computed as follows, where H is head
differential (ft) on the discharge side of the pump:
##EQU00004## The pump efficiency (E) is estimated to range between
0.25 and 0.40. The power consumed by any particular pump can then
be computed directly from pump horsepower using a horsepower to
power unit conversion factor c, which is equal to 0.000746.
MW=Megawatt=HP*0.000746 (13)
Using equations (12) and (13) and setting H=(HeadLoss) (from
equation (9)), a range of possible flow rates (Q) for the pipeline
from minimum possible flow to maximum possible flow for the
pipeline is plotted versus the equivalent expected power
consumption at the monitored pumping station.
For example, for a major U.S. pipeline flowing from the Gulf Coast
to a major U.S. storage hub in Oklahoma, flow rates can range from
0 to 350,000 barrels per day, with the diameter of the pipeline
(D)=2.44 feet. For a monitored pumping station at location x, the
distance of the line (L) from that pumping station to the next
downstream pumping station at location y=368,062 feet. For a
typical mid-range flow rate for the pipeline of 200,000 barrels per
day, the corresponding flow rate Q (gallons/minute)=5833.28. The
kinematic viscosity .nu.=0.004 centiStokes. The elevation
difference between pumping station x and pumping station y=353
feet. The resultant head loss (HeadLoss) is 181.1 feet.
A plot of flow rate (barrels per day) against expected power
consumption (MW) is shown in FIG. 8.
Once such power consumption determinations have been made for any
particular pumping station, power changes at each pumping station
can be correlated to changes in flow through each pumping station.
Thus, since the monitoring devices described above allow for
periodic or continuous measurements of power consumed at a
particular pumping station, the collected data from those
monitoring devices can be used to determine flow through and
between pumping stations.
Once the flow rate between consecutive pumping stations has been
computed, one preferred method of deriving total pipeline flow is
to compute an average of the estimated flow rates at several
pumping stations to determine the flow rate on the pipeline as a
whole. The approach is often used when fewer than half the pumping
stations are monitored on a given pipeline.
Another preferred method of deriving total pipeline flow uses Monte
Carlo simulations to model power usage at all pumping stations
along a given pipeline and is used when half or more than half of
the pumping stations are monitored. The simulations use inputs from
the monitored pumping stations, as well as predictions of power
usage at pumping stations along the pipeline which are not
monitored. The power usage at the unmonitored pumping stations is
modeled with a uniform distribution from zero to a maximum power
usage based on the number of pumps and the type of pumps at each
pumping station. For a given flow value, each Monte Carlo
simulation uses the same observed power usage for the monitored
pumping stations and performs a random sample of the power usage
uniform distributions for the unmonitored pumping stations.
Equations (6) to (13) are used to simulate pressure head profile
along the entire pipeline. If the pressure head profile along the
pipeline goes below the minimum pressure or above the maximum
pressure, the simulation is flagged as invalid. The pipeline flow
regime, zero barrels per day to capacity, is divided in a finite
number of intervals. For each flow value at the center of each flow
interval, a large number of Monte Carlo simulations are performed
and the number of valid simulations is recorded. An overall
pipeline flow is computed using the following expected value:
##EQU00005##
where f_i is i.sup.th flow value v_i is the number of valid
simulations for f_i, and Totv is the total number of valid
simulations for all flow intervals.
Finally, in certain circumstances, it may be impossible or
impractical to monitor real-time electric power consumption of some
number of pumping stations along a selected pipeline. However, it
would still be advantageous to know whether a particular pumping
station was on or off. Accordingly, a thermal imaging camera (like
those used for monitoring storage facilities, as described above)
may be used to assess the on/off condition of one or more pumping
stations. Similarly, although electrically-driven induction motors
are commonly used at pumping stations, some pumps may be driven by
gas or diesel-powered motors. Such motors typically exhaust through
one or more stacks, so the operation and operational levels
(including number of pumps on or off) of the pumping station can
also be assessed using a thermal imaging camera directed at the
stacks or ancillary equipment.
Processing Facilities
Crude oil invariably enters an oil refinery at some point in the
network to be processed into gasoline and/or other petroleum
products, such as diesels, jet fuels, heating oil, etc. The ability
of the various units at the refinery to utilize the incoming crude
oil is dependent on the proper functioning of such units.
Refineries are highly complex facilities which generally are
designed and intended to function year-round on a 24 hour-a-day, 7
day-a-week schedule. However, disruptions and malfunctions of
equipment at these facilities occurs on a relatively frequent basis
and can have immediate impact on market dynamics. Specifically, if
particular units at one or more refineries are off-line, there is a
decreased demand for crude oil at the affected refineries and a
decreased supply of gasoline and other refined products in markets
supplied by affected refineries. So-called refinery unit ramp-downs
and ramp-ups are of particular market interest, but, in addition,
there is also an interest in the flow rates of crude oil into each
refinery and the amount of crude oil in storage at each refinery at
any given time.
Therefore, in the method and system of the present invention, the
operational status of one or more processing facilities, such as
refineries, in the network is ascertained. With respect to the term
"processing facilities," this term is also intended to include any
facility in a network in which there is some handling of the liquid
energy commodity that can be monitored, even if there is no
material change to the liquid energy commodity, such as buffering,
transfer, or surge overflow facilities. In any event, one preferred
method for monitoring the operation of processing facilities is by
using fixed thermal imaging cameras. A thermal imaging camera can
acquire thermal data and record images of emissions and heat
signatures of various key units that can be used to ascertain
whether the processing facility is functioning as expected or
not.
FIGS. 9(a)-(d) are a series of thermal images illustrating the
ramp-down of a fluid catalytic cracking unit (FCCU) at a refinery.
As reflected in FIGS. 9(a)-(d), each primary unit at a refinery
typically has one or more exhaust stacks associated with it, which
generally function as exhausts for heating devices, such as
furnaces, heat exchangers, etc., or exhausts for emission control
devices, such as wet gas scrubbers, electrostatic dust
precipitators, etc. In general, if a particular unit is functioning
normally, a characteristic level of heating is observed on a
thermal image of the stack. In addition, a characteristic emission
via a plume emanating from the top of the stack is also present and
visible. When the unit is turned off, or not operating normally,
the heating and emissions from such stacks are seen to be either
absent completely or display some abnormal characteristics (e.g.,
excess heating or excess emissions). Similarly, aside from the
stacks, a characteristic level of heating can be observed on the
thermal images for many other of types of equipment associated with
a unit, including, but not limited to, vessels, piping, duct work,
heat exchangers, furnaces, and/or ancillary equipment.
Returning to FIGS. 9(a)-(d), in this particular example, the FCCU
is in far right of the image, as illustrated by the arrow. In FIG.
9(a), the FCCU is shown in normal operating mode. As shown in FIG.
9(b), during the start of ramp-down, emissions are seen from a
stack in the middle of the FCCU, and the FCCU itself shows relative
cooling with respect to neighboring units. In FIG. 9(c), the body
of the FCCU shows continuing cooling; the emitting stacks remains
hot, but emissions from it are reduced. In 9(d), both FCCU and the
stack have completely cooled down, and ramp-down of the FCCU is
complete.
Each primary unit at a refinery also has emergency control devices,
such as flares, blowdown stacks, and other devices which can burn
off or dissipate inline streams of feedstocks, processing
chemicals, and associated by-products in the case where the units
need to be shut-down rapidly. Such emergency control devices can
also be used in the normal operation of such units to control the
amounts of feedstocks, processing chemicals, and associated
by-products in the process streams. These emergency control devices
can also be observed by a thermal imaging camera as operating at
characteristically levels (typically low or off) when the
associated units are operating normally and at abnormal levels
(typically emitting at abnormal and elevated levels) when the
associated units are experiencing issues, are being started up, or
being shut down.
In any event, thermal images such as those shown in FIGS. 9(a)-(d)
can be analyzed visually or by using automated image analysis to
ascertain the operational status of the primary units of a
refinery. For further discussion of the image analysis techniques
that may be utilized, reference is made to commonly assigned and
co-pending U.S. patent application Ser. No. 13/269,833 entitled
"Method and System for Providing Information to Market Participants
About One or More Power Generating Units Based on Thermal Image
Data," which is a continuation of U.S. patent application Ser. No.
12/053,139. Each of these patent applications is incorporated
herein by reference.
Additionally, while the above discussion is directed to refineries
which refine crude oil into gasoline and/or other petroleum
products, the monitoring technology is also applicable to such
processing facilities as: (a) fractionation facilities, where NGLs
are separated from the crude oil for subsequent processing into
such products as ethane, propane, and butanes; (b) upgrading
facilities (or upgraders), which process raw crude oils after
mining and prepare the crude oils for delivery to and subsequent
refining at crude oil refineries; (c) ethylene cracking facilities,
where NGL products and/or petroleum liquids (such as naphtha) are
processed into petrochemical industry feedstocks such as ethylene,
propylene, etc.; and (d) natural gas processing facilities, which
produce NGL from natural gas.
Balances
Now, having described the monitoring of the three fundamental
components of a particular network--(i) storage facilities, (ii)
pipelines, and (iii) processing facilities--it is possible to
determine total "balances" of the crude oil or other liquid energy
commodity. For example, and as mentioned above, "balances" of
interest to market participants with respect to crude oil include,
but are not limited to: the amount of crude oil in storage in a
given market region at a given time; the amount of crude oil
flowing into a market region from adjacent market regions; and/or
the amount of crude oil being processed into gasoline and other
petroleum products.
Referring again to FIG. 1, in order to determine the physical
balances of crude oil or other liquid energy commodity in a
particular network, the combined data from the monitoring of these
three fundamental components can be used to estimate physical
balances of interest.
For example, FIG. 10 illustrates a storage hub (i.e., a collection
of storage tanks) 100 that is connected to three pipelines:
Pipeline A, Pipeline B, and Pipeline C. Using the techniques of
analysis described above, measurement of the amount of crude oil in
each storage tank is made, and then a sum of all such measurements
yields the collective amount in storage at the storage hub 100 at a
given time. Then, a determination of the real-time inflows and
outflows of oil into the storage hub 100 can be made on a periodic
basis from the data collected from the monitoring devices for the
power lines supplying electric power to selected pumping stations
along each of the three pipelines. For example, if Pipeline A and
Pipeline B are incoming, and Pipeline C is outgoing, a net inflow
into the storage hub 100 can be computed from a summation of the
inflows less any outflows:
NetInflowIntoHub=(PipelineA.sub.FLOW+PipelineB.sub.FLOW)-PipelineC.sub.FL-
OW (15)
Thus, with the measurement of the collective amount in storage at
the storage hub 100 at a given time and subsequent periodic
determinations of inflows and outflows, a substantially real-time
determination can be made as to the amount of crude oil in storage
at the storage hub 100 at a given time. Furthermore, additional
modeling may then be possible to determine operational parameters,
such as the effect on storage levels at the storage hub 100 for
various operational conditions of the incoming and outgoing
pipelines, the use of certain storage tanks to contain crude oil
from certain pipelines, what crude oil is in transit through the
storage hub 100 and what crude oil stays at the storage hub
100.
For another example, data collected from the monitoring devices for
the power lines supplying electric power to selected pumping
stations (PS1, PS2, PS3, PS4, PS5) along each of the pipelines can
be combined with information obtained from the analysis of thermal
images of a refinery (not shown) connected to the pipelines to
determine the balances of crude oil in transit to the refinery, in
storage at the refinery, and being processed at the refinery at any
given time.
It is further contemplated that, in addition to combining directly
measured data collected at different locations in a particular
network as described above, data can also be obtained from
third-party and publicly available data sources, such as that
provided by the United States Energy Information Administration
("EIA"), to deliver estimations and predictions of parameters of
market interest related to commodity supply, demand, and storage.
For example, one such parameter of interest is the total volume of
crude oil in storage in the PAD 2 market region at any given time.
EIA publishes an amount for this value weekly, typically on
Wednesday morning at 10:30 AM EST. Directly measured data and EIA
data can be effectively combined using a standard mathematical
regression model. Specifically, the standard mathematical
regression model is used to fit the directly measured data to the
historic PAD 2 crude oil storage inventory data published by EIA.
The determined PAD 2 crude oil inventories are then estimated going
forward using the resultant model. Referring now to FIG. 11, in one
example, directly measured data is obtained using the techniques
described above for: (i) the storage levels at a major PAD 2
storage hub; (ii) the crude oil flow rates into PAD 2 (collected
from six pipelines entering the PAD 2 region from PAD 3); and (iii)
refinery unit operational data (collected from nine PAD 2
refineries). This directly measured data is then fitted using a
standard mathematical regression model to historic PAD 2 crude oil
storage inventory data published by EIA. The determined crude oil
inventories based on the model output ("Model" line in FIG. 11) can
then be compared to actual PAD 2 crude oil inventory data ("PAD 2"
line in FIG. 11), and PAD 2 crude oil inventories can then be
estimated going forward using the resultant model.
For another example, FIG. 12 is a schematic view of another
exemplary network associated with crude oil production. In FIG. 12,
crude oil originating from an oil platform 200 (or other production
source) is delivered to a pipeline 210. Along the pipeline 210,
there are four sensor locations--S1, S2, S3, S4, as further
described below in Table 2. The pipeline 210 is then connected and
delivers the crude oil to a fractionation facility 212, which is
monitored by a sensor S5, as also described below in Table 2. From
the fractionation facility 212, crude oil flows to a storage
facility 214, which is monitored by sensor(s) S6, as also described
below in Table 2.
Finally, in this exemplary implementation, there is an additional
data input, as represented in FIG. 12 by S7. This additional data
input, S7, is used to further verify the collected data and results
of the various computational analyses. Specifically, in the
exemplary network shown in FIG. 12, crude oil in the storage
facility 214 is delivered to one or more ships at a sea terminal
for export. Much data about ships that transfer crude oil is
publicly known and available, including ship capacity and ship
location via Automatic Identification System (AIS) ship tracking
services. While a particular ship is in port at the sea terminal, a
visual camera or an infrared camera can be used to estimate flow
rate of oil delivered to the particular ship by measuring the
change in ship draft (i.e., the change in ship position relative to
the waterline) over time. That delivery of oil should be equal to
the reduction in oil level in the storage facility 214. Of course,
such technology can be similarly used when ships are delivering oil
to a storage facility.
TABLE-US-00002 TABLE 2 Monitored Component Sensor Type Output S1
Pumping Power Monitoring Device mW/Flow Rate Station 1 S2 Pumping
Thermal Imaging Camera Operational Status Station 2 (On/Off) S3
Pumping Power Monitoring Device mW/Flow Rate Station 3 S4 Pumping
Thermal Imaging Camera Operational Status Station 4 (On/Off) S5
Fractionation Thermal Imaging Camera Operational Status Facility
(On/Off) S6 Storage Thermal Imaging Camera(s) Level/Volume
Facility/ Tank(s) S7 Ship Visual or Thermal Imaging Draft/Flow Rate
Camera(s)
Referring now to FIG. 13, the outputs from S1, S2, S3, and S4 are
used to determine power changes at each pumping station along the
pipeline 210, which can then be used to determine flow rate of the
crude oil through the pipeline 210, as indicated by block 300 of
FIG. 13, and that flow rate data is stored in a database at a
central data processing facility. The output from S5 is used to
determine the operational status of the fractionation facility 212,
as indicated by block 302 of FIG. 13, and that operational status
information in also stored in database at the central data
processing facility. The output from S6 is used to measure an
amount of the crude oil in storage at the storage facility 214, as
indicated by block 304 of FIG. 13, and that measurement data is
also stored in a database at the central data processing
facility.
At the central data processing facility, an analysis is performed
on the flow rate data, the operational status information, and the
measurement data to determine total "balances" of the crude oil in
different functional parts of the network, as indicated by block
310 of FIG. 13. For example, with respect this exemplary network,
"balances" of interest to market participants would include, but
are not limited to: the amount of crude oil flowing into the
network at a given time, the amount of crude oil in storage in the
network at a given time; and/or the amount of crude oil flowing out
of the network at a given time.
Referring still to FIG. 13, once the analysis has been completed,
information about the balance of the crude oil in the network can
be communicated to market participants and other interested
parties, i.e., third parties who would not ordinarily have ready
access to such information, as indicated by block 320. It is
contemplated and preferred that such communication to third-party
market participants could be achieved through electronic mail
delivery and/or through export of the data to an access-controlled
Internet web site, which third-party market participants can access
through a common Internet browser program, such as Microsoft
Internet Explorer.RTM.. Of course, communication of information and
data to third-party market participants may also be accomplished
through a wide variety of other known communications media without
departing from the spirit and scope of the present invention.
FIG. 14 is a schematic representation of the core components in an
exemplary implementation of the method and system of the present
invention. As shown in FIG. 14, the central data processing
facility 10 includes a first database 20, a second database 22, and
a third database 24. Of course, these databases 20, 22, 24 could be
integrated into a single database at the central data processing
facility 10. Furthermore, the central data processing facility 10
hosts a digital computer program, i.e., computer-readable
instructions stored and executed by a computer, that includes
appropriate modules for executing the requisite routines and
subroutines for performing the operational steps of the present
invention. Thus, an exemplary system for determining an amount of a
liquid energy commodity stored in a tank in accordance with the
present invention includes: (a) a storage measurement module 40 for
receiving and analyzing collected images of one or more storage
facilities to measure an amount of the liquid energy commodity in
storage at each of the one or more storage facilities, and storing
that measurement data in a first database 20; (b) a flow rate
determination module 42 for receiving and processing measurements
of the electric potential and magnetic flux densities associated
with power lines for pumping stations on a pipeline to determine a
flow rate of the liquid energy commodity in each selected pipeline,
and storing that flow rate data in a second database 22; (c) an
operational status module 44 for receiving and processing
information about an operational status of a processing facility
and storing that operational status information in a third database
24; (d) an analysis module 50 for querying the databases 20, 22, 24
and analyzing the measurement data, the flow rate data, and the
operational status information to determine a balance of the liquid
energy commodity in the network or a selected portion thereof at a
given time; and (e) a communications module 60 for communicating
information about the liquid energy commodity to a third-party
market participant.
One of ordinary skill in the art will recognize that additional
embodiments and implementations are also possible without departing
from the teachings of the present invention. This detailed
description, and particularly the specific details of the exemplary
embodiments and implementations disclosed therein, is given
primarily for clarity of understanding, and no unnecessary
limitations are to be understood therefrom, for modifications will
become obvious to those skilled in the art upon reading this
disclosure and may be made without departing from the spirit or
scope of the invention.
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